Part 1: Measuring Sea Level Rise Over Time


Map of Redwood City’s Boundaries 

## [1] "This is the flood map of Redwood City"


Here we can see an image of the Redwood City flood basin. Clearly this area is due to experience damaging effects from sea level rise

Part 2: Building Exposure in Redwood City

Out of total households, 81 of them did not have any vehicles available in 2020. 919 of them had 1 vehicle.

##   vehicle_count year no_vehicles one_vehicle
## 1      5645.000 2020     81.0000     919.000
## 2      7997.906 2030    114.7618    1302.051
## 3      9487.590 2040    136.1372    1544.569
## 4     10106.098 2050    145.0122    1645.262


Assuming each city in San Mateo is increasing vehicle count by the same amount each decade, we can use EMFAC data to determine how many vehicles there will be in our flood risk zone between 2020-2050. In 2020 there is expected to be 5645 cars, in 2030 there is expected to be 7997.906, in 2030 there is expected be 9487.590, and in 2050 there is expected to be 10106.098 vehicles. Households with no vehicles in our study area is also projected to increase by the same percentage and same with households with one vehicle. When determining flood risk for these vehicles, we need to remember that we are using household flood risk as our test, so it will look like there is little to no risk for those households with 1/0 vehicles. Obviously, this is not the case, however, this particular model is looking at vehicle-related flood damage and thus cannot capture accurately the damage incurred by households.

Part 4: Vulnerability Data


we are able to use building damage as a proxy for vehicle damage with the assumption that street level or basement flooding would affect cars as well as houses. We swapped the average depth of exposure to 0.5 ft instead of the -2 in the textbook example.


The more flood depth, the greater the vehicle flood damage as can be seen in this plot.

Part 5: Average Anualized Cost of Floods


Risk Estimation


In order to compute the damage function we determined that damage is equal to the average cost of a vehicle (determined by resale value computed by the New York Times https://www.nytimes.com/2021/03/25/business/car-paint-job-resale-value.html) and mulitply that by perc_damage and the number of cars that will be flooded and the number of vehicles per building. Per the research, 88% of vehicles would be moved to higher ground if given 12 hours notice of a flood, therefore we need to factor that into expected damage.
This table shows us the damage for vehicle exposure in redwood city. Clearly OSM 233058123 has the highest $ damage risk.
Here we have the “$ damage” for each vehicle, for each event, we can now combine each trio of storm events together (for each of 5 levels of sea level rise). The result will be an “average annualized loss” for each vehicle for 5 different hypothetical years, each of which has a different base sea level rise.


For each of our study OSM areas we have a dollar value assigned to the damage. Clearly OSM 233058123 has the highest cost related to flood risk.


Next, we will consider, for any given year (we’ll bound our analysis to the 2020-2050 range), the likelihood of sea level rise being some amount or greater. Intuitively for the current year, the current sea level rise is what it is, and the chances of any greater amount of sea level rise before the end of the year are effectively 0. 10 years from now, the distribution of probabilities will be something, based on climate models. 

## # A tibble: 10 × 10
##      SLR `2020` `2030` `2040` `2050` `2060` `2070` `2080` `2090` `2100`
##    <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
##  1     0  0.942  0.923  0.793  0.508  0.235  0.094  0.033  0.011  0.005
##  2    25  0      0.051  0.198  0.453  0.581  0.44   0.249  0.128  0.071
##  3    50  0      0      0.001  0.035  0.176  0.363  0.409  0.313  0.19 
##  4    75  0      0      0      0      0.007  0.099  0.224  0.296  0.29 
##  5   100  0      0      0      0      0      0.004  0.075  0.162  0.219
##  6   125  0      0      0      0      0      0      0.01   0.064  0.126
##  7   150  0      0      0      0      0      0      0      0.025  0.055
##  8   175  0      0      0      0      0      0      0      0.001  0.034
##  9   200  0      0      0      0      0      0      0      0      0.01 
## 10   500  0      0      0      0      0      0      0      0      0


Now we have projected flood risk between 2020-2050 and its associated $ damage. It seems like 2020 and 2030 are going to be the most costly years with their associated osm_id’s. 

## # A tibble: 4 × 2
##   year    damage
##   <chr>    <dbl>
## 1 2020    28095.
## 2 2030   289945.
## 3 2040  1049334.
## 4 2050  2587136.


The $ damage function per year during our study interval can be seen in the table above. The damage increases exponentially each decade.


When toggling between 2020 and 2050, we can see that there is always a flood risk in this zone that is only exarcerbated over time with some buildings and vehicles becoming more exposed in the inlet close to the Bayshore Freeway (some being bright orange/red). The peninsula with Saginew Dr in the center seems to be most at risk in 2050 with the greatest change between 2020 and 2050. The Redwood Shores Lagoon neighborhood is at serious risk of flood damage though none of the buildings seem to have as urgent or dire a flood risk as the Maple st neighborhood. Luckily, there does seem to be a fair amount of buffer zone between the water and housing. This may be because we did not include industrial use buildings and if we did there would be greater red zones.

Obviously there are a lot of assumptions in our study and a lot of projection, but clearly Redwood City needs to strengthen its climate change/sea level rise mitigation plans.


Redwood AAL 

## # A tibble: 3 × 2
##   GEOID            aal
## * <chr>          <dbl>
## 1 060816103021  45370.
## 2 060816103032 346568.
## 3 060816103034 490361.
## [1] "$882,298.1"


Across our three block groups, these are the averaage anual loss in dollars values. The block group with the highest AAL is the Redwood Shores Lagoon area, which is consistent with the previous map.


Here we can visually see the AAL across our block groups. The darkest chunk has the middle-most amount of buildings but the greatest loss, the orange has the most buildings and a little less loss, the white chunk has the least amount of buildings and the least amount of loss. This probably has to do with the fact that the white chunk is the most inland versus the darkest chunk which is closest to the water and the canals.Waterfront properties generally are more expensive than inland ones so it would makes sense that despite there being a fair amount of buildings, there is a huge loss compared to the middle chunk. Even at the block level, however, our results may not be granular enough to fully understand the placement of the buildings and their associated AALs. This would be an interesting starting point to look at cost of housing, housing tenancy, and AAL affects.